# Build a classification task using 3 informative featuresX,y=make_classification(n_samples=1000,n_features=25,n_informative=3,n_redundant=2,n_repeated=0,n_classes=8,n_clusters_per_class=1,random_state=0)# Create the RFE object and compute a cross-validated score.svc=SVC(kernel="linear")# The "accuracy" scoring is proportional to the number of correct# classificationsrfecv=RFECV(estimator=svc,step=1,cv=StratifiedKFold(2),scoring='accuracy')rfecv.fit(X,y)print("Optimal number of features : %d"%rfecv.n_features_)